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Unverified Commit f1e53807 authored by Illia Silin's avatar Illia Silin Committed by GitHub
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Merge branch 'develop' into ck_host_lib

parents 7450417d d9f1ead3
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Showing with 812 additions and 288 deletions
example/01_gemm/gemm_xdl_streamk.cpp 100644 → 100755
View file @ f1e53807
......@@ -15,7 +15,6 @@ using F16 = ck::half_t;
using ALayout = Row;
using BLayout = Row;
// using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
......
example/01_gemm/run_gemm_example.inc
View file @ f1e53807
......@@ -5,88 +5,6 @@
#include "ck/tensor_operation/gpu/device/device_gemm_streamk.hpp"
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 2e-1;
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 2e-1;
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 2e-1;
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 2e-1;
}
else
{
return 1e-3;
}
}
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
......@@ -143,8 +61,8 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
switch(config.init_method)
{
case 0:
ck::utils::FillConstant<ADataType>{static_cast<ADataType>(1.f)}(a_m_k);
ck::utils::FillConstant<BDataType>{static_cast<BDataType>(1.f)}(b_k_n);
ck::utils::FillConstant<ADataType>{ck::type_convert<ADataType>(1.f)}(a_m_k);
ck::utils::FillConstant<BDataType>{ck::type_convert<BDataType>(1.f)}(b_k_n);
break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
......
example/01_gemm/run_gemm_example_streamk_v2.inc
View file @ f1e53807
......@@ -3,88 +3,6 @@
#pragma once
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
......@@ -176,6 +94,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_ref_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
......@@ -196,6 +115,8 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_ref_buf(sizeof(CDataType) *
c_m_n_device_ref_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
......@@ -240,6 +161,13 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
return true;
}
std::size_t workspace_size = gemm.GetWorkSpaceSize(&argument);
if(workspace_size != 0)
{
workspace.Realloc(workspace_size);
gemm.SetWorkSpacePointer(&argument, workspace.GetDeviceBuffer());
}
bool pass = true;
if((config.do_verification == 1) || (config.do_verification == 3))
{
......@@ -271,6 +199,36 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
#endif
}
if((config.do_verification == 2) || (config.do_verification == 3))
{
// GPU verification
auto ref_gemm_gpu = ReferenceGemmInstanceGPU{};
auto ref_invoker_gpu = ref_gemm_gpu.MakeInvoker();
auto ref_argument_gpu = ref_gemm_gpu.MakeArgument(
static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_ref_buf.GetDeviceBuffer()),
M,
N,
K,
a_element_op,
b_element_op,
c_element_op);
std::cout << "Running verification on GPU." << std::endl;
ref_invoker_gpu.Run(ref_argument_gpu, StreamConfig{});
c_m_n_device_ref_buf.FromDevice(c_m_n_device_ref_result.mData.data());
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_device_ref_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
}
if(config.time_kernel)
{
ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
......
example/01_gemm/run_gemm_example_v2.inc
View file @ f1e53807
......@@ -3,88 +3,6 @@
#pragma once
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
......@@ -261,7 +179,7 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
if(config.time_kernel)
{
ave_time =
invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 5, 10, true, 4});
invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 50, 100, true, 4});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
......
example/04_gemm_add_add_fastgelu/CMakeLists.txt
View file @ f1e53807
......@@ -16,7 +16,7 @@ if(USE_BITINT_EXTENSION_INT4)
add_example_dependencies(example_gemm_add_add_fastgelu_xdl example_gemm_add_add_fastgelu_xdl_int4)
endif(USE_BITINT_EXTENSION_INT4)
list(APPEND gpu_list gfx90a gfx940 gfx941 gfx942)
list(APPEND gpu_list gfx90a gfx940 gfx941 gfx942 gfx950)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
......
example/15_grouped_gemm/grouped_gemm_multiple_d_splitk_xdl_fp16.cpp
View file @ f1e53807
......@@ -186,15 +186,15 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
}
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<DDataType, 0>{});
}
}
}
......@@ -246,7 +246,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
// do GEMM
auto argument = gemm.MakeArgument(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, cde_element_op);
gemm.SetKBatchSize(argument, config.k_batch);
gemm.SetKBatchSize(&argument, config.k_batch);
if(!gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
......@@ -257,7 +257,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
gemm.SetWorkSpacePointer(&argument, gemm_workspace_dev.GetDeviceBuffer());
DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
gemm.SetDeviceKernelArgs(&argument, gemm_arg_dev_mem.GetDeviceBuffer());
invoker.Run(argument, StreamConfig{nullptr, false, 1});
......
example/15_grouped_gemm/grouped_gemm_multiple_d_xdl_fp16.cpp
View file @ f1e53807
......@@ -91,7 +91,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
{
auto group_count = problem_size.group_count;
using KernelArguments = ck::tensor_operation::device::GroupedGemmTileLoopKernelArguments<NumDs>;
using KernelArguments = ck::tensor_operation::device::GroupedGemmKernelArgument<NumDs>;
using GemmDesc = ck::tensor_operation::device::GemmDesc;
// GEMM shape
......@@ -190,15 +190,15 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
for(int j = 0; j < NumDs; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0});
}
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
for(int j = 0; j < NumDs; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<DDataType, 0>{});
}
}
}
......
example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_bias_fp16.cpp
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
......@@ -167,11 +167,11 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
}
d0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
d0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<D0DataType, 1>{});
}
using GroupedGemmKernelArgument = ck::tensor_operation::device::GroupedGemmKernelArgument<1>;
......@@ -254,7 +254,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
gemm.GetDeviceKernelArgSize(&argument),
hipMemcpyHostToDevice));
gemm.SetDeviceKernelArgs(argument, gemm_kernel_args_dev.GetDeviceBuffer());
gemm.SetDeviceKernelArgs(&argument, gemm_kernel_args_dev.GetDeviceBuffer());
gemm.SetKBatch(argument, config.k_batch);
invoker.Run(argument, StreamConfig{nullptr, false});
......
example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16.cpp
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
......@@ -157,8 +157,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
}
}
......@@ -239,7 +239,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
"not support this GEMM problem");
}
gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
gemm.SetDeviceKernelArgs(&argument, gemm_arg_dev_mem.GetDeviceBuffer());
gemm.SetKBatch(argument, config.k_batch);
invoker.Run(argument, StreamConfig{nullptr, false});
......
example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16_fp8.cpp
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
......@@ -158,8 +158,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
}
}
......@@ -240,7 +240,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
"not support this GEMM problem");
}
gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
gemm.SetDeviceKernelArgs(&argument, gemm_arg_dev_mem.GetDeviceBuffer());
gemm.SetKBatch(argument, config.k_batch);
invoker.Run(argument, StreamConfig{nullptr, false});
......
example/15_grouped_gemm/run_grouped_gemm_example.inc
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
struct ProblemSize final
......@@ -124,8 +127,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
}
}
......@@ -168,9 +171,23 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
auto argument = gemm.MakeArgument(
p_a, p_b, p_Ds, p_c, gemm_descs, a_element_op, b_element_op, c_element_op);
DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
std::size_t workspace_size = gemm.GetWorkSpaceSize(&argument);
std::size_t kargs_size = gemm.GetDeviceKernelArgSize(&argument);
DeviceMem gemm_workspace, gemm_kargs;
gemm.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
// The following is necessary since TwoStage kernel is using additional memory both
// for Workspace and kernel arguments.
if(kargs_size > 0)
{
gemm_kargs.Realloc(kargs_size);
gemm.SetDeviceKernelArgs(&argument, gemm_kargs.GetDeviceBuffer());
}
if(workspace_size > 0 && workspace_size != kargs_size)
{
gemm_workspace.Realloc(workspace_size);
gemm.SetWorkSpacePointer(&argument, gemm_workspace.GetDeviceBuffer());
}
if(!gemm.IsSupportedArgument(argument))
{
......
example/18_batched_gemm_reduce/CMakeLists.txt
View file @ f1e53807
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942 gfx950)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
......
example/21_gemm_layernorm/gemm_xdl_layernorm_naive_single_kernel_fp16.cpp
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
......@@ -175,8 +175,8 @@ int main(int argc, char* argv[])
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
a_m_k.GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
b_k_n.GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
}
c0_n_bias.GenerateTensorValue(GeneratorTensor_2<C0DataType>{-5, 5});
......
example/24_batched_gemm/CMakeLists.txt
View file @ f1e53807
......@@ -22,3 +22,6 @@ if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_batched_gemm_xdl_int4 batched_gemm_xdl_int4.cpp)
add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_int4)
endif()
add_example_executable(example_batched_gemm_xdl_fp16int4_b_scale_v3 batched_gemm_xdl_fp16int4_b_scale_v3.cpp)
add_example_dependencies(example_batched_gemm_xdl example_batched_gemm_xdl_fp16int4_b_scale_v3)
example/24_batched_gemm/batched_gemm_xdl_bf16_v3.cpp
View file @ f1e53807
......@@ -78,14 +78,14 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmMultiD
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
0, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
0, // BBlockLdsExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
......
example/24_batched_gemm/batched_gemm_xdl_fp16int4_b_scale_v3.cpp 0 → 100644
View file @ f1e53807
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <numeric>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_xdl_fpAintB_b_scale.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using BDataType = ck::pk_i4_t;
using BScaleDataType = ck::half_t;
using AccDataType = F32;
using CShuffleDataType = F16;
using CDataType = F16;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto PermuteA = false;
static constexpr bool PermuteB = false;
static constexpr ck::index_t Scale_Block_N = 1;
static constexpr ck::index_t Scale_Block_K = 128;
static constexpr ck::index_t KPerBlock = 256;
// clang-format off
using DeviceBatchedGemmV2Instance =
ck::tensor_operation::device::DeviceBatchedGemm_Xdl_CShuffleV3_BScale<
ALayout, BLayout, CLayout,
ADataType, BDataType, BScaleDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CElementOp, GemmDefault,
256, Scale_Block_N, Scale_Block_K,
16, 64,
KPerBlock, 8, 32,
16, 16,
1, 1,
S<32, 8, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, CDataType, CDataType, PermuteA, PermuteB>;
// clang-format on
using ReferenceBatchedGemmInstance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
AccDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
#include "run_batched_gemm_example_fp16int4_b_scale.inc"
int main(int argc, char* argv[]) { return !run_batched_gemm_fp16_int4_b_scale_example(argc, argv); }
example/24_batched_gemm/run_batched_gemm_example_fp16int4_b_scale.inc 0 → 100644
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include <random>
#pragma once
struct ProblemSize final
{
ck::index_t M = 128;
ck::index_t N = 128;
ck::index_t K = 384;
ck::index_t stride_A = K;
ck::index_t stride_B = K;
ck::index_t stride_C = N;
ck::index_t batch_stride_A = M * K;
ck::index_t batch_stride_B = K * N;
ck::index_t batch_stride_C = M * N;
// Batched Gemm count
ck::index_t batch_count = 2;
// Split K count
ck::index_t KBatch = 1;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = true;
};
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
bool run_batched_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M,
N,
K,
stride_A,
stride_B,
stride_C,
batch_stride_A,
batch_stride_B,
batch_stride_C,
batch_count,
KBatch] = problem_size;
auto f_host_tensor_descriptor = [](std::size_t batch_count_,
std::size_t row,
std::size_t col,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({batch_count_, row, col}, {batch_stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor({batch_count_, row, col}, {batch_stride, 1_uz, stride});
}
};
ck::index_t Scale_Stride_BN = (K + Scale_Block_K - 1) / Scale_Block_K;
ck::index_t batch_BScale_Stride =
((K + Scale_Block_K - 1) / Scale_Block_K) * ((N + Scale_Block_N - 1) / Scale_Block_N);
Tensor<ADataType> a_g_m_k(
f_host_tensor_descriptor(batch_count, M, K, stride_A, batch_stride_A, ALayout{}));
Tensor<BDataType> b_g_k_n(
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, BLayout{}));
Tensor<BDataType> b_g_k_n_permute(
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, BLayout{}));
Tensor<BScaleDataType> b1_g_k_n(
f_host_tensor_descriptor(batch_count,
(K + Scale_Block_K - 1) / Scale_Block_K,
(N + Scale_Block_N - 1) / Scale_Block_N,
Scale_Stride_BN,
batch_BScale_Stride,
BLayout{}));
switch(config.init_method)
{
case 0:
a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_g_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
case 1:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
break;
case 2:
a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
case 3:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_g_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
case 4:
a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_g_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
break;
case 5:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
default:
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.5, 0.5});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
}
Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, CLayout{}));
Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, CLayout{}));
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
std::cout << "b1_g_k_n: " << b1_g_k_n.mDesc << std::endl;
std::cout << "c_g_m_n: " << c_g_m_n_host_result.mDesc << std::endl;
DeviceMem a_g_m_k_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_g_k_n_device_buf(sizeof(BDataType) * b_g_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem b1_g_scale_device_buf(sizeof(BScaleDataType) * b1_g_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_g_m_n_device_buf(sizeof(CDataType) *
c_g_m_n_device_result.mDesc.GetElementSpaceSize());
printf("a_g_m_k size: %zu, b_g_k_n size: %zu, b1_g_k_n size: %zu, c_g_m_n size: %zu\n",
a_g_m_k.mDesc.GetElementSpaceSize(),
b_g_k_n_permute.mDesc.GetElementSpaceSize(),
b1_g_k_n.mDesc.GetElementSpaceSize(),
c_g_m_n_device_result.mDesc.GetElementSpaceSize());
// weight permute
if constexpr(PermuteB)
{
printf("Permute B\n");
int K1 = KPerBlock;
int K0 = K / KPerBlock;
// int K0, N, K1
for(int bs = 0; bs < batch_count; bs++)
{
for(int j = 0; j < K0; j++)
{
for(int i = 0; i < N; i++)
{
for(int jj = 0; jj < K1; jj++)
{
b_g_k_n_permute(bs * batch_stride_B + j * N * K1 + i * K1 + jj) =
b_g_k_n(bs * batch_stride_B + i * K + (j * K1 + jj));
}
}
}
}
}
else
{
b_g_k_n_permute = b_g_k_n;
}
// vector pk_i4x4 permute
for(int bs = 0; bs < batch_count; bs++)
{
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j += 8)
{
int input[8];
for(int k = 0; k < 4; k++)
{
int i4x2 = b_g_k_n_permute(bs, j + k * 2, i).data;
input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
}
// permute 01234567->20643175
{
int hi = input[2];
int lo = input[0];
int i4x2 = (hi << 4) | lo;
b_g_k_n_permute(bs, j + 0, i) = i4x2;
}
{
int hi = input[6];
int lo = input[4];
int i4x2 = (hi << 4) | lo;
b_g_k_n_permute(bs, j + 2, i) = i4x2;
}
{
int hi = input[3];
int lo = input[1];
int i4x2 = (hi << 4) | lo;
b_g_k_n_permute(bs, j + 4, i) = i4x2;
}
{
int hi = input[7];
int lo = input[5];
int i4x2 = (hi << 4) | lo;
b_g_k_n_permute(bs, j + 6, i) = i4x2;
}
}
}
}
a_g_m_k_device_buf.ToDevice(a_g_m_k.mData.data());
b_g_k_n_device_buf.ToDevice(b_g_k_n_permute.mData.data());
b1_g_scale_device_buf.ToDevice(b1_g_k_n.mData.data());
DeviceMem workspace;
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
auto gemm = DeviceBatchedGemmV2Instance{};
auto invoker = gemm.MakeInvoker();
float ave_time = 0;
auto argument =
gemm.MakeArgument(static_cast<ADataType*>(a_g_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_g_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_g_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
stride_A,
stride_B,
stride_C,
Scale_Stride_BN,
batch_stride_A,
batch_stride_B,
batch_stride_C,
batch_BScale_Stride,
static_cast<BScaleDataType*>(b1_g_scale_device_buf.GetDeviceBuffer()),
batch_count, // batch count
KBatch, // split K count
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
bool pass = true;
Tensor<float> b_g_k_n_dequant({batch_count, K, N});
if(config.do_verification)
{
float v_b = 0;
for(int bs = 0; bs < batch_count; bs++)
{
for(int n = 0; n < N; n++)
{
for(int k = 0; k < K; k++)
{
ck::pk_i4_t i4x2 = b_g_k_n(bs, k, n).data;
int8_t i4 = 0;
if(k % 2 == 1)
i4 = (i4x2.data >> 0) & 0xf;
else
i4 = (i4x2.data >> 4) & 0xf;
i4 = i4 - 8;
v_b = ck::type_convert<float>(i4);
b_g_k_n_dequant(bs, k, n) =
ck::type_convert<float>(v_b) *
ck::type_convert<float>(b1_g_k_n(bs, k / Scale_Block_K, n / Scale_Block_N));
}
}
}
auto ref_gemm = ReferenceBatchedGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_g_m_k,
b_g_k_n_dequant,
c_g_m_n_host_result,
PassThrough{},
PassThrough{},
PassThrough{});
ref_invoker.Run(ref_argument);
ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
hip_check_error(hipDeviceSynchronize());
c_g_m_n_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
pass &= ck::utils::check_err(c_g_m_n_device_result,
c_g_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
}
if(config.time_kernel)
{
ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N /
(ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
#if 0
// print A matrix
printf("A matrix:\n");
for(int bs = 0; bs < batch_count; bs++)
{
printf("batch %d -> Address: %p\n", bs, static_cast<void*>(&a_g_m_k(bs, 0, 0)));
for(int i = 0; i < M; i++)
{
for(int j = 0; j < K; j++)
{
printf("%.2f,", static_cast<float>(a_g_m_k(bs, i, j)));
}
printf("\n");
}
}
// print B matrix original
printf("B matrix original:\n");
for(int bs = 0; bs < batch_count; bs++)
{
printf("batch %d -> Address: %p\n", bs, static_cast<void*>(&b_g_k_n(bs, 0, 0)));
for(int n = 0; n < N; n++)
{
for(int k = 0; k < K; k++)
{
ck::pk_i4_t i4x2 = b_g_k_n(bs, k, n).data;
int8_t i4 = 0;
if(k % 2 == 1)
i4 = (i4x2.data >> 0) & 0xf;
else
i4 = (i4x2.data >> 4) & 0xf;
i4 = i4 - 8;
printf("%d,", static_cast<int>(i4));
}
printf("\n");
}
}
// print B matrix
printf("B matrix:\n");
for(int bs = 0; bs < batch_count; bs++)
{
printf("batch %d -> Address: %p\n", bs, static_cast<void*>(&b_g_k_n_dequant(bs, 0, 0)));
for(int i = 0; i < K; i++)
{
for(int j = 0; j < N; j++)
{
printf("%.2f, ", static_cast<float>(b_g_k_n_dequant(bs, i, j)));
}
printf("\n");
}
}
// print B scale matrix
printf("B Scale matrix:\n");
for(int bs = 0; bs < batch_count; bs++)
{
printf("batch %d -> Address: %p\n", bs, static_cast<void*>(&b1_g_k_n(bs, 0, 0)));
for(int i = 0; i < (K + Scale_Block_K - 1) / Scale_Block_K; i++)
{
for(int j = 0; j < (N + Scale_Block_N - 1) / Scale_Block_N; j++)
{
printf("%.2f, ", static_cast<float>(b1_g_k_n(bs, i, j)));
}
printf("\n");
}
}
// print C matrix
printf("C matrix:\n");
for(int bs = 0; bs < batch_count; bs++)
{
printf(
"batch %d -> Address: %p\n", bs, static_cast<void*>(&c_g_m_n_device_result(bs, 0, 0)));
for(int i = 0; i < M; i++)
{
for(int j = 0; j < N; j++)
{
printf("%.2f, ", static_cast<float>(c_g_m_n_device_result(bs, i, j)));
}
printf("\n");
}
}
printf("C reference matrix:\n");
for(int bs = 0; bs < batch_count; bs++)
{
printf("batch %d -> Address: %p\n", bs, static_cast<void*>(&c_g_m_n_host_result(bs, 0, 0)));
for(int i = 0; i < M; i++)
{
for(int j = 0; j < N; j++)
{
printf("%.2f, ", static_cast<float>(c_g_m_n_host_result(bs, i, j)));
}
printf("\n");
}
}
#endif
return pass;
}
bool run_batched_gemm_fp16_int4_b_scale_example(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
std::mt19937 gen(11939);
std::uniform_int_distribution<int> dis(0, 15);
problem_size.M = 128 * (dis(gen) + 1);
problem_size.N = 128 * (dis(gen) + 1);
problem_size.K = 256 * (dis(gen) + 2);
problem_size.batch_count = 2;
if(argc == 4)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
else if(argc >= 7)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
problem_size.M = std::stoi(argv[4]);
problem_size.N = std::stoi(argv[5]);
problem_size.K = std::stoi(argv[6]);
if(argc >= 8)
{
problem_size.batch_count = std::stoi(argv[7]);
}
if(argc >= 9)
{
problem_size.KBatch = std::stoi(argv[8]);
}
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
exit(0);
}
problem_size.stride_A = problem_size.K;
problem_size.stride_B = problem_size.K;
problem_size.stride_C = problem_size.N;
problem_size.batch_stride_A = problem_size.M * problem_size.K;
problem_size.batch_stride_B = problem_size.K * problem_size.N;
problem_size.batch_stride_C = problem_size.M * problem_size.N;
return run_batched_gemm(problem_size, config);
}
example/30_grouped_conv_fwd_multiple_d/run_grouped_conv_fwd_bias_relu_add_example.inc
View file @ f1e53807
......@@ -32,6 +32,56 @@ using BiasLayout = typename LayoutSettingSelector<NDimSpatial>::BiasLayout;
template <ck::index_t NDimSpatial>
using ResidualLayout = typename LayoutSettingSelector<NDimSpatial>::ResidualLayout;
#if defined(CK_USE_AMD_MFMA_GFX950)
template <ck::index_t NDimSpatial>
using DeviceConvFwdInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
NDimSpatial,
InputLayout<NDimSpatial>,
WeightLayout<NDimSpatial>,
ck::Tuple<BiasLayout<NDimSpatial>, ResidualLayout<NDimSpatial>>,
OutputLayout<NDimSpatial>,
InKernelDataType,
WeiKernelDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<BiasKernelDataType, ResidualKernelDataType>,
OutKernelDataType,
InElementOp,
WeiElementOp,
OutElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
1, //
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
64, // KPerBlock
16, // AK1
16, // BK1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
4, // ABlockTransferSrcScalarPerVector
4, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
4, // BBlockTransferSrcScalarPerVector
4, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
1,
1,
S<1, 16, 1, 16>,
4>;
#else // defined(CK_USE_AMD_MFMA_GFX950)
template <ck::index_t NDimSpatial>
using DeviceConvFwdInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
......@@ -80,6 +130,7 @@ using DeviceConvFwdInstance =
1,
S<1, 16, 1, 16>,
4>;
#endif // defined(CK_USE_AMD_MFMA_GFX950)
template <ck::index_t NDimSpatial>
using HostConvFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
......
example/31_batched_gemm_gemm/CMakeLists.txt
View file @ f1e53807
......@@ -5,6 +5,6 @@ if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_batched_gemm_gemm_xdl_int4 batched_gemm_gemm_xdl_int4.cpp)
endif(USE_BITINT_EXTENSION_INT4)
if(NOT GPU_TARGETS MATCHES "gfx94" AND NOT GPU_TARGETS MATCHES "gfx1")
if(NOT GPU_TARGETS MATCHES "gfx94" AND NOT GPU_TARGETS MATCHES "gfx95" AND NOT GPU_TARGETS MATCHES "gfx1")
add_example_executable(example_batched_gemm_gemm_xdl_int8 batched_gemm_gemm_xdl_int8.cpp)
endif()
example/31_batched_gemm_gemm/run_batched_gemm_gemm_example.inc
View file @ f1e53807
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -150,7 +150,7 @@ bool run_batched_gemm_gemm_example(int argc, char* argv[])
break;
default:
a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
}
......
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